The objective of the proposed research is to develop a comprehensive set of statistical procedures for analyzing data on workplace exposure to contaminants. The statistical problems to be investigated include: calibration problems, detection limits and tolerance limits. These problems will be studied in the context of models that include random effects, multi- variate models, and some alternative models that better describe low levels of workplace exposure (compared to linear regression models.) The proposed research work for these models is motivated by two considerations: (I) typical exposure data cannot be treated as a simple random sample from a homogeneous population and linear regression models are very often inadequate, especially at very low concentrations of the contaminant; and (ii) in the context of the suggested models, very little work has been done on the issues of calibrations, detection limits and tolerance limits. Results applicable to finite samples are mostly lacking. This calls for a thorough and comprehensive investigation of the above issues in the context of the suggested models. The development of results for finite samples will be a major goal. The proposed research based on the suggested models is expected to result in methodology that is better suited and more accurate for exposure monitoring in a wide variety of workplace environments.